{"title":"Whose Advice is Credible? Claiming Lay Expertise in a Covid-19 Online Community.","authors":"Larry Au, Gil Eyal","doi":"10.1007/s11133-021-09492-1","DOIUrl":null,"url":null,"abstract":"<p><p>During the initial months of the Covid-19 pandemic, credentialed experts-scientists, doctors, public health experts, and policymakers-as well as members of the public and patients faced radical uncertainty. Knowledge about how Covid-19 was spread, how best to diagnose the disease, and how to treat infected patients was scant and contested. Despite this radical uncertainty, however, certain users of <i>Covid-19 Together</i>, a large online community for those who have contracted Covid-19, were able to dispense advice to one another that was seen as credible and trustworthy. Relying on Goffman's dramaturgical theory of social interaction, we highlight the performative dimension of claims to lay expertise to show how credibility is accrued under conditions of radical uncertainty. Drawing on four months of data from the forum, we show how credible performances of lay expertise necessitated the entangling of expert discourse with illness experience, creating a hybrid interlanguage. A credible performance of lay expertise in this setting was characterized by users' ability to switch freely between personal and scientific registers, finding and creating resonances between the two. To become a credible lay expert on this online community, users had to learn to ask questions and demonstrate a willingness to engage with biomedical knowledge while carefully generalizing their personal experience.</p>","PeriodicalId":47710,"journal":{"name":"Qualitative Sociology","volume":"45 1","pages":"31-61"},"PeriodicalIF":2.1000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8564268/pdf/","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Qualitative Sociology","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1007/s11133-021-09492-1","RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/11/3 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"SOCIOLOGY","Score":null,"Total":0}
引用次数: 13
Abstract
During the initial months of the Covid-19 pandemic, credentialed experts-scientists, doctors, public health experts, and policymakers-as well as members of the public and patients faced radical uncertainty. Knowledge about how Covid-19 was spread, how best to diagnose the disease, and how to treat infected patients was scant and contested. Despite this radical uncertainty, however, certain users of Covid-19 Together, a large online community for those who have contracted Covid-19, were able to dispense advice to one another that was seen as credible and trustworthy. Relying on Goffman's dramaturgical theory of social interaction, we highlight the performative dimension of claims to lay expertise to show how credibility is accrued under conditions of radical uncertainty. Drawing on four months of data from the forum, we show how credible performances of lay expertise necessitated the entangling of expert discourse with illness experience, creating a hybrid interlanguage. A credible performance of lay expertise in this setting was characterized by users' ability to switch freely between personal and scientific registers, finding and creating resonances between the two. To become a credible lay expert on this online community, users had to learn to ask questions and demonstrate a willingness to engage with biomedical knowledge while carefully generalizing their personal experience.
期刊介绍:
Qualitative Sociology is dedicated to the qualitative interpretation and analysis of social life. The journal does not restrict theoretical or analytical orientation and welcomes manuscripts based on research methods such as interviewing, participant observation, ethnography, historical analysis, content analysis and others which do not rely primarily on numerical data.